用开源代码展示机器学习如何自动分析声学数据
Machine Learning in Acoustics: A Review and Open-Source Repository
- 基于Python实现多种机器学习方法处理声学信号
- 涵盖分类、生成与物理约束神经网络等典型应用
- 提供可复现的Jupyter示例，适合科研与工程人员
声学数据在生物声学、通信及海洋与地球科学等领域提供重要科学与工程洞察。本文综述机器学习（ML）在声学中的最新进展及其变革潜力，包括深度学习（DL）。利用Python高级编程语言，我们演示了广泛适用的机器学习技术，可自动检测和发现声学数据中的模式，用于分类、回归与生成任务。具体案例包括声学数据分类、空间音频生成建模以及物理信息神经网络。本工作还推出了AcousticsML项目，一个部署于GitHub的实用Jupyter笔记本集合，展示机器学习在声学中的优势，鼓励研究者与从业者采用可复现的数据驱动方法应对声学挑战。
原文摘要 · Abstract (English)
Acoustic data provide scientific and engineering insights in fields ranging from bioacoustics and communications to ocean and earth sciences. In this review, we survey recent advances and the transformative potential of machine learning (ML) in acoustics, including deep learning (DL). Using the Python high-level programming language, we demonstrate a broad collection of ML techniques to detect and find patterns for classification, regression, and generation in acoustics data automatically. We have ML examples including acoustic data classification, generative modeling for spatial audio, and physics-informed neural networks. This work includes AcousticsML, a set of practical Jupyter notebook examples on GitHub demonstrating ML benefits and encouraging researchers and practitioners to apply reproducible data-driven approaches to acoustic challenges.
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